Neural Network Facial Authentication for Public Electric Vehicle Charging Station

TL;DR

This paper compares the facial recognition accuracy of Dlib ResNet and KNN classifiers on Asian faces using HOG features, demonstrating a practical application for EV charging station user authentication.

Contribution

It provides a comparative analysis of Dlib ResNet and KNN for facial recognition on Asian faces, highlighting accuracy differences in a real-world EV charging context.

Findings

Dlib ResNet shows lower accuracy on Asian faces compared to KNN.

HOG features are used for facial vector extraction in both methods.

The study demonstrates the feasibility of facial recognition for EV station access.

Abstract

This study is to investigate and compare the facial recognition accuracy performance of Dlib ResNet against a K-Nearest Neighbour (KNN) classifier. Particularly when used against a dataset from an Asian ethnicity as Dlib ResNet was reported to have an accuracy deficiency when it comes to Asian faces. The comparisons are both implemented on the facial vectors extracted using the Histogram of Oriented Gradients (HOG) method and use the same dataset for a fair comparison. Authentication of a user by facial recognition in an electric vehicle (EV) charging station demonstrates a practical use case for such an authentication system.